






"""simple docstring"""
import faiss  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import requests  # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn  # noqa: F401 # Here to have a nice missing dependency error message early on
import tqdm  # noqa: F401 # Here to have a nice missing dependency error message early on
from mauve import compute_mauve  # From: mauve-text

import datasets


snake_case : Tuple     		=		"""\
@inproceedings{pillutla-etal:mauve:neurips2021,
  title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},
  author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},
  booktitle = {NeurIPS},
  year      = {2021}
}

"""

snake_case : Optional[Any]     		=		"""\
MAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.

MAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.

For details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).

This metrics is a wrapper around the official implementation of MAUVE:
https://github.com/krishnap25/mauve
"""

snake_case : Dict     		=		"""
Calculates MAUVE scores between two lists of generated text and reference text.
Args:
    predictions: list of generated text to score. Each predictions
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
Optional Args:
    num_buckets: the size of the histogram to quantize P and Q. Options: 'auto' (default) or an integer
    pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1
    kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9
    kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5
    kmeans_max_iter: maximum number of k-means iterations. Default 500
    featurize_model_name: name of the model from which features are obtained. Default 'gpt2-large' Use one of ['gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl'].
    device_id: Device for featurization. Supply a GPU id (e.g. 0 or 3) to use GPU. If no GPU with this id is found, use CPU
    max_text_length: maximum number of tokens to consider. Default 1024
    divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25
    mauve_scaling_factor: \"c\" from the paper. Default 5.
    verbose: If True (default), print running time updates
    seed: random seed to initialize k-means cluster assignments.
Returns:
    mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,
    frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,
    divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,
    p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,
    q_hist: same as above, but with q_text.
Examples:

    >>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest
    >>> import datasets
    >>> mauve = datasets.load_metric('mauve')
    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP
    >>> print(out.mauve) # doctest: +SKIP
    1.0
"""



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION							,	_KWARGS_DESCRIPTION)
class        UpperCamelCase__ (				datasets.Metric):







    """simple docstring"""







    def 				a__			(		self :	Union[str, Any] ):





           '''simple docstring'''

           return datasets.MetricInfo(
               description=_DESCRIPTION				,    citation=_CITATION				,    homepage='https://github.com/krishnap25/mauve'				,    inputs_description=_KWARGS_DESCRIPTION				,    features=datasets.Features(
                   {
                       'predictions': datasets.Value('string'				,    id='sequence' ),
                       'references': datasets.Value('string'				,    id='sequence' ),
                   } )				,    codebase_urls=['https://github.com/krishnap25/mauve']				,    reference_urls=[
                   'https://arxiv.org/abs/2102.01454',
                   'https://github.com/krishnap25/mauve',
               ]				,    )







    def 				a__			(		self :	List[Any]				,    UpperCamelCase_ :	List[Any]				,    UpperCamelCase_ :	Any				,    UpperCamelCase_ :	List[str]=None				,    UpperCamelCase_ :	str=None				,    UpperCamelCase_ :	Optional[int]=None				,    UpperCamelCase_ :	Tuple=None				,    UpperCamelCase_ :	List[str]="auto"				,    UpperCamelCase_ :	Union[str, Any]=-1				,    UpperCamelCase_ :	Tuple=0.9				,    UpperCamelCase_ :	Tuple=5				,    UpperCamelCase_ :	List[str]=5_0_0				,    UpperCamelCase_ :	Optional[Any]="gpt2-large"				,    UpperCamelCase_ :	List[str]=-1				,    UpperCamelCase_ :	Union[str, Any]=1_0_2_4				,    UpperCamelCase_ :	Optional[Any]=2_5				,    UpperCamelCase_ :	Optional[int]=5				,    UpperCamelCase_ :	Any=True				,    UpperCamelCase_ :	Optional[Any]=2_5				,    ):





           '''simple docstring'''

           __magic_name__      				=    compute_mauve(
               p_text=UpperCamelCase_				,    q_text=UpperCamelCase_				,    p_features=UpperCamelCase_				,    q_features=UpperCamelCase_				,    p_tokens=UpperCamelCase_				,    q_tokens=UpperCamelCase_				,    num_buckets=UpperCamelCase_				,    pca_max_data=UpperCamelCase_				,    kmeans_explained_var=UpperCamelCase_				,    kmeans_num_redo=UpperCamelCase_				,    kmeans_max_iter=UpperCamelCase_				,    featurize_model_name=UpperCamelCase_				,    device_id=UpperCamelCase_				,    max_text_length=UpperCamelCase_				,    divergence_curve_discretization_size=UpperCamelCase_				,    mauve_scaling_factor=UpperCamelCase_				,    verbose=UpperCamelCase_				,    seed=UpperCamelCase_				,    )
           return out







"""simple docstring"""
import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer


snake_case : List[Any]     		=		{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

snake_case : List[Any]     		=		{
    """vocab_file""": {
        """unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/vocab.txt""",
    },
    """tokenizer_file""": {
        """unc-nlp/lxmert-base-uncased""": (
            """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/tokenizer.json"""
        ),
    },
}

snake_case : Optional[int]     		=		{
    """unc-nlp/lxmert-base-uncased""": 5_1_2,
}

snake_case : List[Any]     		=		{
    """unc-nlp/lxmert-base-uncased""": {"""do_lower_case""": True},
}



class        UpperCamelCase__ (				a_):







    """simple docstring"""
    __UpperCAmelCase           = VOCAB_FILES_NAMES
    __UpperCAmelCase           = PRETRAINED_VOCAB_FILES_MAP
    __UpperCAmelCase           = PRETRAINED_INIT_CONFIGURATION
    __UpperCAmelCase           = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __UpperCAmelCase           = LxmertTokenizer







    def __init__(		self :	Union[str, Any]				,    UpperCamelCase_ :	List[str]=None				,    UpperCamelCase_ :	Dict=None				,    UpperCamelCase_ :	Optional[int]=True				,    UpperCamelCase_ :	Dict="[UNK]"				,    UpperCamelCase_ :	str="[SEP]"				,    UpperCamelCase_ :	Dict="[PAD]"				,    UpperCamelCase_ :	Optional[int]="[CLS]"				,    UpperCamelCase_ :	Any="[MASK]"				,    UpperCamelCase_ :	Any=True				,    UpperCamelCase_ :	List[Any]=None				,    **UpperCamelCase_ :	Dict				,    ):





           '''simple docstring'''

           super().__init__(
               UpperCamelCase_				,    tokenizer_file=UpperCamelCase_				,    do_lower_case=UpperCamelCase_				,    unk_token=UpperCamelCase_				,    sep_token=UpperCamelCase_				,    pad_token=UpperCamelCase_				,    cls_token=UpperCamelCase_				,    mask_token=UpperCamelCase_				,    tokenize_chinese_chars=UpperCamelCase_				,    strip_accents=UpperCamelCase_				,    **UpperCamelCase_				,    )

           __magic_name__      				=    json.loads(self.backend_tokenizer.normalizer.__getstate__() )
           if (
               normalizer_state.get('lowercase'				,    UpperCamelCase_ ) != do_lower_case
               or normalizer_state.get('strip_accents'				,    UpperCamelCase_ ) != strip_accents
               or normalizer_state.get('handle_chinese_chars'				,    UpperCamelCase_ ) != tokenize_chinese_chars
           ):
                  __magic_name__      				=    getattr(UpperCamelCase_				,    normalizer_state.pop('type' ) )
                  __magic_name__      				=    do_lower_case
                  __magic_name__      				=    strip_accents
                  __magic_name__      				=    tokenize_chinese_chars
                  __magic_name__      				=    normalizer_class(**UpperCamelCase_ )

           __magic_name__      				=    do_lower_case







    def 				a__			(		self :	Dict				,    UpperCamelCase_ :	Optional[Any]				,    UpperCamelCase_ :	Optional[int]=None ):





           '''simple docstring'''

           __magic_name__      				=    [self.cls_token_id] + token_ids_a + [self.sep_token_id]

           if token_ids_a:
                  output += token_ids_a + [self.sep_token_id]

           return output







    def 				a__			(		self :	int				,    UpperCamelCase_ :	List[int]				,    UpperCamelCase_ :	Optional[List[int]] = None ):





           '''simple docstring'''

           __magic_name__      				=    [self.sep_token_id]
           __magic_name__      				=    [self.cls_token_id]
           if token_ids_a is None:
                  return len(cls + token_ids_a + sep ) * [0]
           return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]







    def 				a__			(		self :	Dict				,    UpperCamelCase_ :	str				,    UpperCamelCase_ :	Optional[str] = None ):





           '''simple docstring'''

           __magic_name__      				=    self._tokenizer.model.save(UpperCamelCase_				,    name=UpperCamelCase_ )
           return tuple(UpperCamelCase_ )